Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) represents one promising tool in the fight against breast cancer, prostate cancer, and other cancer types. In DCE-MRI, a contrast agent known to significantly and predictably enhance certain MRI readings, such as T1-weighted MRI readings, is injected into the patient and a time sequence of MRI volumes is acquired. As the contrast agent, commonly termed a tracer, is transported throughout the body by the vascular system (e.g., arteries, arterioles, capillaries, veins, and other types of blood vessels), it diffuses across the vessel walls into the surrounding tissue. The surrounding tissue generally comprises (i) tissue cells and (ii) interstitial space among the tissue cells, termed extracellular extravascular space (EES). The tracer, which in one example comprises gadolinium and diethylenetriamine penta-acetic acid (Gd-DTPA), is selected such that it “washes into” the EES by diffusion across the vessel walls but does not enter the tissue cells.
Initially, the tracer “washes into” the EES because its concentration is higher inside the vessel walls (i.e., in the blood plasma) than outside the vessel walls (i.e., in the EES). However, as the concentration of the tracer in the blood plasma becomes increasingly diluted, it reaches a point where the tracer concentration in the blood plasma becomes less than in the EES, after which the tracer begins to “wash out” from the EES by diffusing across the vessel walls back into the blood plasma. It has been found that local tissue characteristics, including characteristics that may be associated with cancer or other tissue abnormalities, can highly affect the particular local time dynamics of the tracer wash-in and wash-out processes. Qualitative visual study of the time sequence of MRI volumes can yield some insight into such local tissue characteristics. However, it is the general goal of DCE-MRI processing algorithms to quantitatively study the local time dynamics of the tracer wash-in and wash-out processes for uncovering clues to the presence or absence of cancer or other abnormalities in the body part under study.
Because of the microscopic nature of vascular structures at the cellular scale, the individual compartments (plasma and EES) generally cannot be individually imaged because of voxel resolution limitations. Rather, for any particular voxel, only a value related (indirectly) to a total average tracer concentration Ct(t) within that voxel is truly measurable. At any particular voxel, the relationship between the total average tracer concentration Ct(t) (often simply termed the total tracer concentration), the plasma tracer concentration Cp(t), and the EES tracer concentration Ci(t) is provided by Eq. {1} below.Ct(t)=vpCp(t)+viCi(t)   {1}
In Eq. {1}, vp represents the percentage of total volume occupied by plasma, while vi represents the percentage of total volume occupied by EES.
In the context of DCE-MRI, local tissue physiology is often expressed in terms of one or more pharmacokinetic parameters that characterize one or more features of the tracer wash-in and wash-out process for that local tissue region. Examples of DCE-MRI pharmacokinetic parameters include a transfer constant Ktrans, sometimes termed a permeability constant, and an extracellular volume parameter ve, representing the percentage of all tissue volume lying outside the tissue cells (ve=vp+vi), where tracer diffusion across the vessel walls is characterized by the first-order diffusion equation of Eq. {2} below.
                                          ⅆ                                          C                i                            ⁡                              (                t                )                                                          ⅆ            t                          =                                            K              trans                                      v              e                                ⁡                      [                                                            C                  p                                ⁡                                  (                  t                  )                                            -                                                C                  i                                ⁡                                  (                  t                  )                                                      ]                                              {        2        }            
The relationship of Eq. {2} applies under a presumption that vi, the percentage of total volume occupied by EES (often in the range of 10%-40%), is substantially greater than vp, the percentage of total volume occupied by plasma (often in the range of 2%-3%), in which case the extracellular volume parameter ve substantially approximates vi.
When viewed as a dynamical system with blood plasma tracer concentration Cp(t) as the single input and extracellular tissue region Ci(t) as the single output, the dynamical system defined by Eq. {2} is a first-order system with a time constant equal to Ktrans/ve and an impulse response h(t) (the hypothetical value of Ci(t) if the input Cp(t) is assumed to be a unit impulse spike at time t=0) that is a decaying exponential, as illustrated in FIG. 1A. In a DCE-MRI PK parameter context, the time constant of the decaying exponential of FIG. 1A is often termed a rate constant kep, which is thus defined according to the relationship of Eq. {3}:
                              k          ep                =                              K            trans                                v            e                                              {        3        }            
As is evident from Eq. {3}, determination of any two of the PK parameters Ktrans, ve, and kep inherently determines the third. For the so-called “two-compartment” characterization of Eq. {2}, it is a goal of DCE-MRI processing to numerically determine, for each voxel, the PK parameters Ktrans, ve, and kep by numerically processing a sequence of MRI volumes acquired at sequence of times subsequent to the injection of the tracer into the patient. One or more MRI volumes acquired prior to the tracer injection can also be used in conjunction with the post-injection MRI volumes. The inputs to the DCE-MRI processing algorithm are the set of MRI volumes and, for each MRI volume, the instant in time at which that MRI volume was acquired relative to the time of the tracer injection or other suitable reference time. The output of the DCE-MRI processing algorithm comprises three data volumes containing the values Ktrans, ve, and kep, respectively, for each voxel in the imaged volume.
FIGS. 1B and 1C conceptually illustrate one additional PK parameter t0, termed a bolus arrival time difference. In particular, FIG. 1B illustrates a conceptual side view of a voxelwise map of tracer concentration values, a plasma reference region 106, and a typical voxel 108. It is often a key assumption for many DCE-MRI processing algorithms that the plasma concentration curve Cp(t) is relatively uniform across the imaged tissue volume, and therefore that knowledge or estimation of the plasma concentration Cp(t) at one location, such as a reference location 106, can also be used to represent the plasma concentration Cp(t) at another location, such as at the voxel 108, during the computation algorithms that compute the PK parameters. Moreover, if particular reference regions 106 are known, based on their location in the anatomy, to have known “textbook” relationship estimates between their total tracer concentrations Ct(t) and their plasma tracer concentrations Cp(t) (e.g., the left ventricle, or the pectoral muscle, or the spleen), then the plasma concentration Cp(t) can be estimated from the total concentration Ct, 106(t) at those locations, and then used volume-wide as an estimate of the plasma concentration Cp(t) during computation of the PK parameters. The term plasma reference region is used herein to refer to such regions. The known “textbook” relationships are often provided in the form of the PK parameters themselves, which can be looked up for the left ventricle, pectoral muscle, spleen, etc., but which (of course) are valid only for those particular voxels in those locations.
However, there is often a delay between when the tracer material from a bolus tracer dose passes through the reference region 106 and when it passes through the voxel 108, which is termed the bolus arrival time difference t0 for the voxel 108. As illustrated in FIG. 1C, this bolus arrival time difference t0 can be readily incorporated into the impulse response h(t) for the voxel 108 and computationally uncovered by the PK computation algorithm along with the other PK parameters Ktrans, ve, and kep. Also, local variations in t0 can be a further clue to the presence or absence of cancer or other abnormality at that locality.
From a practical perspective, the measurability of variations in t0 with respect to the tissue anomalies that can be detected can serve as rough dividing lines between what is considered a slow MRI sequence and what is considered a fast MRI sequence. It has been found, for example, that there can be differences (Δt0) in the bolus arrival time difference t0 of about 4 seconds between malignant breast tissue and adjacent benign prostate tissue, and it is therefore desirable to acquire MRI sample volumes at a rate that is at least 4 seconds per sample volume in order to detect this difference. For the prostate, it has been found that there can be differences (Δt0) in t0 of about 1 second between malignant prostate tissue and adjacent benign prostate tissue and it is therefore desirable to acquire MRI sample volumes at a rate that is at least 1 second per sample volume in order to detect this difference. Rates slower than 4 seconds per sample for the breast and 1 second per sample for the prostate may thus serve as rough dividing lines between what is considered a slow MRI sequence versus a fast MRI sequence. Although one or more of the preferred embodiments hereinbelow is particularly advantageous for fast MRI sequences, it is to be appreciated that the scope of the preferred embodiments is not so limited and also extends to PK parameter computation for slow MRI sequences as well.
Many of the challenges in DCE-MRI arise because the voxel values of the data volumes actually received from the MRI system, which can be termed raw MRI volumes, unprocessed MRI volumes, or MRI reading volumes, are only indirectly related to the tracer concentration Ct(t). FIG. 2 illustrates an approach to PK parameter computation according to a prior art method. At step 202, a time sequence of MRI reading volumes 203A-203E associated with a bolus tracer dose injection at a time tB is received. Another MRI volume 201 corresponding to a different MR sequence type acquired prior to the bolus tracer dose injection may also be received. The MRI volume 201 is generally in spatial registration with the MRI reading volumes 203A-203E, although their voxel resolutions might be different.
At step 204, a first tissue region useful for machine calibration is identified, usually in a manual manner by a radiologist. By way of example, the radiologist may manually circle (e.g., using a mouse) fat tissue on a slicewise display of the MRI volume 201 or, equivalently, one of the MRI reading volumes 203A-203E. At step 206, T1 values for the volume voxels are computed using the identified fat voxels of the first tissue region, a universally known T1 value for fat tissue (265 ms), and known MRI system parameters used during the volume acquisitions. The MRI volume 201 corresponding to the different MR sequence type may also be used at step 206. Then, at step 208, the T1 values are converted to total tracer concentration values Ct(t) for each voxel. Accordingly, upon execution of step 208, there has been computed a volume 209 which, for each voxel, contains a time sequence of tracer concentration values for that voxel, as illustrated in FIG. 2.
At step 210, a second tissue region is identified that is a plasma reference region. Because the voxels of the second tissue region are identified as plasma reference regions, then their total tracer concentration Ct(t), which was computed at step 208, can be used to compute the plasma tracer concentration Cp(t) based on the known “textbook” values of Ktrans, kep, and t0. For each voxel, by virtue of the two-compartment model and Eq. {2}, the total tracer concentration Ct(t) corresponds to a convolution of the plasma tracer concentration Cp(t) with the impulse response h(t) of FIG. 1A or FIG. 1C. Generally speaking, known prior art methods use deconvolution-based approaches to compute Cp(t) based on their knowledge of Ct(t) and h(t), the latter being known (estimated) by virtue of “textbook” values of Ktrans, ve, kep, and t0. Upon completion of step 210, there has been computed a time function Cp(t) that can be used in computing the PK parameters in conjunction with the voxelwise Ct(t) curves computed at step 208. Again in view of the two-compartment model and Eq. {2}, which dictates that Ct(t) is the convolution Cp(t) with h(t) at each voxel, at step 212 sample points for the function h(t) are computed for each voxel using a samplewise deconvolution of the total tracer concentration Ct(t) computed at step 208 and the plasma tracer concentration Cp(t) determined at step 210. Finally, at step 214, for each voxel, the PK parameters Ktrans, ve, kep, and t0 are parametrically fitted to the sample points of h(t) that were computed at step 212.
One disadvantage of the method of FIG. 2 is that the deconvolution process is highly sensitive to noise and/or errors in the voxel data, which can reduce the accuracy of the results, introduce visual noise into visual maps of the parameters, or even become unstable and fail to converge altogether. Another disadvantage of the method of FIG. 2 relates to the identified plasma reference region used to estimate the plasma tracer concentration, the algorithm being sensitive to errors associated with the selection of the region or errors/noise in the raw voxel data. Still another disadvantage is that the deconvolution process is highly computationally intensive, requiring the use of more expensive hardware plafforms.
The method of FIG. 2 also has disadvantages with respect to the practical usability of the system, especially when the often-preferable manual method is used for selection of the machine calibration reference tissue region (e.g., fat) and/or selection of the plasma reference tissue region. In particular, the highly computationally intensive deconvolution process needs to be performed after the reference tissue is identified. Accordingly, there can be an undesirably long wait time between when the radiologist circles the reference region and when the final PK parameters are ready for display. This can be especially problematic if the radiologist wishes to interactively alter a reference tissue selection previously made, because the same long wait time needs to be endured after any change to the selection. It is to be appreciated that although particularly advantageous in conjunction with an interactive manual reference tissue identification process, the scope of the preferred embodiments described infra is not so limited.
More generally, other issues arise in the context of the DCE-MRI environment that are at least partially addressed by one or more of the preferred embodiments described herein. By way of example, the DCE-MRI process produces a large set of new data that, upon examination, may be indicative of an abnormal condition. Especially in view of the large amount of data already made available by the MRI medical imaging modality, new problems arise relating to increased workload, increased professional liability risk, decreased patient throughput, and increased per-patient cost.
Accordingly, it would be desirable to provide for more precise computation of PK parameters in a DCE-MRI environment in a manner that is more robust and stable against noise in the acquired MRI volumes and/or other input data errors.
It would be further desirable to provide such precise, robust, and stable computation of PK parameters in a manner that is faster, usable on less expensive hardware platforms, and more amenable to real-time, interactive user displays.
It would be still further desirable to provide an easy-to-use and easy-to-read interactive display for presenting PK parameters and/or other DCE-MRI related information.
It would be even further desirable to provide such PK parameter computation and/or interactive DCE-MRI-related display in a manner that is highly adaptable for, or otherwise applicable to, a variety of different body parts.
It would be still further desirable to compute and/or provide DCE-MRI-related data in a manner that addresses one or more of the workload, professional liability, and per-patient cost issues associated with the increased volume of data that is being made available. Other issues arise as would be apparent to one skilled in the art in view of the present disclosure.